NECSTSpecial Talk - Everything You Always Wanted to Know About Continuous Querying but You Never Dared to Ask
NECSTSpecial Talk
Prof. Riccardo Tommasini
INSA, Lyon
DEIB - NECSTLab Meeting Room (Building 20)
On Line via Facebook
April 17th, 2023
3.00 pm
Contacts:
Marco Santambrogio
Research Line:
System architectures
Prof. Riccardo Tommasini
INSA, Lyon
DEIB - NECSTLab Meeting Room (Building 20)
On Line via Facebook
April 17th, 2023
3.00 pm
Contacts:
Marco Santambrogio
Research Line:
System architectures
Sommario
On Monday April 17th, 2023 at 3.00 pm, a new appointment of NECSTSpecialTalk titled "Everything You Always Wanted to Know About Continuous Querying but You Never Dared to Ask" will be held by Riccardo Tommasini, Associate Professor at INSA Lyon in the LIRIS Lab, in DEIB NECSTLab Meeting Room.
The emergence of data streams due to the availability of sensor networks, microservices, and cloud-edge infrastructures has shifted the computational paradigm from data at rest to data in motion, putting stream data management at the centre of the research agenda. In the original vision, SDM refers to a set of techniques for reacting fast to changes in data, detecting events, and identifying patterns and trends. Systems for SDM (DSMSs) were opposed to Database Management Systems as they can handle stream unboundedness. In particular, the family of queries evaluated by DSMS is called Continuous Queries, a form of stream processing that aims to answer users' information needs over time, until explicitly stopped. On the other hand, Stream Processing Systems (SPS) emerged in the last decade have been focusing more on scalability, fault-tolerance, and state management, as agenda as demanded by the Big Data initiative. Given the recent demand for such systems to evolve beyond analytics, as shown by the resurgence of declarative languages for stream processing, continuous querying is relevant as it was never before. Thus, it is a timely moment for diving into the topic, discussing its theoretical underpinnings, existing languages, and algebras, as well as understanding the system counterparts.
The emergence of data streams due to the availability of sensor networks, microservices, and cloud-edge infrastructures has shifted the computational paradigm from data at rest to data in motion, putting stream data management at the centre of the research agenda. In the original vision, SDM refers to a set of techniques for reacting fast to changes in data, detecting events, and identifying patterns and trends. Systems for SDM (DSMSs) were opposed to Database Management Systems as they can handle stream unboundedness. In particular, the family of queries evaluated by DSMS is called Continuous Queries, a form of stream processing that aims to answer users' information needs over time, until explicitly stopped. On the other hand, Stream Processing Systems (SPS) emerged in the last decade have been focusing more on scalability, fault-tolerance, and state management, as agenda as demanded by the Big Data initiative. Given the recent demand for such systems to evolve beyond analytics, as shown by the resurgence of declarative languages for stream processing, continuous querying is relevant as it was never before. Thus, it is a timely moment for diving into the topic, discussing its theoretical underpinnings, existing languages, and algebras, as well as understanding the system counterparts.
Biografia
Ricardo Tommasini is an maitre de conference (Associate Professor) at INSA Lyon. Pior to join LIRIS lab, he was an assistant professor at the University of Tartu, Estonia. Riccardo holds a Ph.D. from the Department of Electronics and Information of the Politecnico di Milano.
His thesis, titled *Velocity on the Web*, investigates the velocity aspects that concern the Web environment. His research interests span Stream Processing, Knowledge Graphs, Logics, and Programming Languages. Since 2015, Riccardo has been a regular attendee and speakers at international venues like ISWC, ICWE, ESWC, DEBS, EDBT and TheWebConf.
On the industrial side, in addition to the collaborations with InfluxData, Neo4j, Confluent,
he spoke at recognised industrial setting DockerCon, LinuxLab, and several major meetups, e.g., Time Series Meetup Tallin (200+ attendants), Kafka Meetup Milan (60+ attendants), and Data Science Seminars Tartu (200+ online attendants). Riccardo's teaching activities span from everything data-related like Knowledge and Data Engineering, Big Data Management, and Databases.
The event will be held online by Facebook.
His thesis, titled *Velocity on the Web*, investigates the velocity aspects that concern the Web environment. His research interests span Stream Processing, Knowledge Graphs, Logics, and Programming Languages. Since 2015, Riccardo has been a regular attendee and speakers at international venues like ISWC, ICWE, ESWC, DEBS, EDBT and TheWebConf.
On the industrial side, in addition to the collaborations with InfluxData, Neo4j, Confluent,
he spoke at recognised industrial setting DockerCon, LinuxLab, and several major meetups, e.g., Time Series Meetup Tallin (200+ attendants), Kafka Meetup Milan (60+ attendants), and Data Science Seminars Tartu (200+ online attendants). Riccardo's teaching activities span from everything data-related like Knowledge and Data Engineering, Big Data Management, and Databases.
The event will be held online by Facebook.
